Events or processes are observed and described by effects, which can be described by measuring the values of certain physical or chemical parameters at a certain time and at certain locations in space. Generally, the data acquired from these measurements are useful for determining the current condition of the event or process or generally speaking of the phenomena. When complex phenomena are considered it may not be at once clear if all the possible effects have been considered or if further effects derive or are related to the phenomena under study. Furthermore, if the phenomena are distributed over a certain area, than it is possible that not every location has been considered or is known or appears to be relevant for measuring the parameters describing the consequences of a phenomenon.
The possible additional parameters and/or the locations at which the effects of the phenomenon will arise may in general not be determined directly by analysing the phenomenon due to the high degree of complexity and no-linearity of the laws governing the phenomenon.
Giving an answer to the above problem is relevant for generating a machine, which can analyze and describe phenomena in an automatic and objective way without the need of entering the in the highly complex and non linear mechanism ruling the phenomena, and also for enhancing the cognitive capacities of devices having a certain level or type of artificial intelligence. Indeed the most challenging technical problem of artificial intelligence is providing means, which enable a device having artificial intelligence to evaluate measured data for determining the most probable consequences and thus taking decisions on how to react to the measured data. This problem is simple until the relationship between the measured data and the space/time evolution of an event or process can be expressed with exact equations. Human intelligence, however, is provided with a skill which allows extracting probable consequences from data that are apparently not clearly related one to the other. The way of determining these consequences is highly non linear and non deterministic, such that it is not a straight manner of implementing or trying to simulate such skills in a device, even if at a very primitive level.
Furthermore, when phenomena are very complex and involve a large number of variables, then even the human skill of giving a heuristic answer to the problem of describing the phenomenon is not sufficient.
Many events or processes can be described by a map, in which characteristic data are represented by points on the map. Such data relates to the measured values of physical and or chemical parameters univocally describing the status at a certain time at which said measure has been carried out.
Currently, there exists a method of determining the relationships between said points, which is known with the denomination of Minimum Spanning Tree. According to this method, for every distribution of points in a D-dimensional space it is possible to determine at least one minimum spanning tree. The minimum spanning tree is the smallest sum of the distances of the points according to certain connections between each point and another point of the map.
A more rigorous mathematical definition is the following:
Given a connected, undirected graph, a spanning tree of that graph is a sub graph which is a tree and connects all the vertices together. A single graph can have many different spanning trees. It is possible to assign a weight to each edge, which is a number representing how unfavorable it is, and use these weights to assign a weight to a spanning tree by computing the sum of the weights of the edges in that spanning tree. A minimum spanning tree (MST) or minimum weight spanning tree is then a spanning tree with a weight less than or equal to the weight of every other spanning tree.
The MST and several algorithms are well known in the art and are common general knowledge of the skilled person.